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Description of Statistical Computation

Author's title

Author*Unverified author*
R Software Modulerwasp_smp.wasp
Title produced by softwareStandard Deviation-Mean Plot
Date of computationThu, 31 Dec 2009 02:34:11 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Dec/31/t12622521307k8nhuk8g9f248z.htm/, Retrieved Thu, 02 May 2024 03:34:18 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=71427, Retrieved Thu, 02 May 2024 03:34:18 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact167

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Exercise 1.13] [Workshop 1 vraag 3.3] [2009-10-06 19:12:27] [309ee52d0058ff0a6f7eec15e07b2d9f]
F RMPD  [Univariate Data Series] [Workshop 2] [2009-10-12 19:59:05] [309ee52d0058ff0a6f7eec15e07b2d9f]
-  MPD    [Univariate Data Series] [wisselkoers US do...] [2009-12-30 15:10:05] [23722951c28e05bb35cc9a97084fe0d9]
- RMPD        [Standard Deviation-Mean Plot] [Paper SDMP] [2009-12-31 09:34:11] [3ebad5d90a5c8606f133189c73066208] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
1.2991
1.3408
1.3119
1.3014
1.3201
1.2938
1.2694
1.2165
1.2037
1.2292
1.2256
1.2015
1.1786
1.1856
1.2103
1.1938
1.202
1.2271
1.277
1.265
1.2684
1.2811
1.2727
1.2611
1.2881
1.3213
1.2999
1.3074
1.3242
1.3516
1.3511
1.3419
1.3716
1.3622
1.3896
1.4227
1.4684
1.457
1.4718
1.4748
1.5527
1.575
1.5557
1.5553
1.577
1.4975
1.4369
1.3322
1.2732
1.3449
1.3239
1.2785
1.305
1.319
1.365
1.4016
1.4088
1.4268
1.4562
1.4816
1.4914

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 2 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=71427&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]2 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=71427&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=71427&T=0

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Standard Deviation-Mean Plot
SectionMeanStandard DeviationRange
11.267750.04972951382684680.1393
21.2352250.03941264506359720.102500000000000
31.34430.0390734273806730.1346
41.496191666666670.07182071691920010.2448
51.3653750.06922116235792210.2084

\begin{tabular}{lllllllll}
\hline
Standard Deviation-Mean Plot \tabularnewline
Section & Mean & Standard Deviation & Range \tabularnewline
1 & 1.26775 & 0.0497295138268468 & 0.1393 \tabularnewline
2 & 1.235225 & 0.0394126450635972 & 0.102500000000000 \tabularnewline
3 & 1.3443 & 0.039073427380673 & 0.1346 \tabularnewline
4 & 1.49619166666667 & 0.0718207169192001 & 0.2448 \tabularnewline
5 & 1.365375 & 0.0692211623579221 & 0.2084 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=71427&T=1

[TABLE]
[ROW][C]Standard Deviation-Mean Plot[/C][/ROW]
[ROW][C]Section[/C][C]Mean[/C][C]Standard Deviation[/C][C]Range[/C][/ROW]
[ROW][C]1[/C][C]1.26775[/C][C]0.0497295138268468[/C][C]0.1393[/C][/ROW]
[ROW][C]2[/C][C]1.235225[/C][C]0.0394126450635972[/C][C]0.102500000000000[/C][/ROW]
[ROW][C]3[/C][C]1.3443[/C][C]0.039073427380673[/C][C]0.1346[/C][/ROW]
[ROW][C]4[/C][C]1.49619166666667[/C][C]0.0718207169192001[/C][C]0.2448[/C][/ROW]
[ROW][C]5[/C][C]1.365375[/C][C]0.0692211623579221[/C][C]0.2084[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=71427&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=71427&T=1

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Standard Deviation-Mean Plot
SectionMeanStandard DeviationRange
11.267750.04972951382684680.1393
21.2352250.03941264506359720.102500000000000
31.34430.0390734273806730.1346
41.496191666666670.07182071691920010.2448
51.3653750.06922116235792210.2084






Regression: S.E.(k) = alpha + beta * Mean(k)
alpha-0.106994534405879
beta0.119876154116665
S.D.0.0575906713069131
T-STAT2.08152034689470
p-value0.128815338716801

\begin{tabular}{lllllllll}
\hline
Regression: S.E.(k) = alpha + beta * Mean(k) \tabularnewline
alpha & -0.106994534405879 \tabularnewline
beta & 0.119876154116665 \tabularnewline
S.D. & 0.0575906713069131 \tabularnewline
T-STAT & 2.08152034689470 \tabularnewline
p-value & 0.128815338716801 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=71427&T=2

[TABLE]
[ROW][C]Regression: S.E.(k) = alpha + beta * Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]-0.106994534405879[/C][/ROW]
[ROW][C]beta[/C][C]0.119876154116665[/C][/ROW]
[ROW][C]S.D.[/C][C]0.0575906713069131[/C][/ROW]
[ROW][C]T-STAT[/C][C]2.08152034689470[/C][/ROW]
[ROW][C]p-value[/C][C]0.128815338716801[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=71427&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=71427&T=2

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Regression: S.E.(k) = alpha + beta * Mean(k)
alpha-0.106994534405879
beta0.119876154116665
S.D.0.0575906713069131
T-STAT2.08152034689470
p-value0.128815338716801






Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha-3.81114431583424
beta2.93034017436506
S.D.1.52925837657265
T-STAT1.91618383083995
p-value0.151203607862385
Lambda-1.93034017436506

\begin{tabular}{lllllllll}
\hline
Regression: ln S.E.(k) = alpha + beta * ln Mean(k) \tabularnewline
alpha & -3.81114431583424 \tabularnewline
beta & 2.93034017436506 \tabularnewline
S.D. & 1.52925837657265 \tabularnewline
T-STAT & 1.91618383083995 \tabularnewline
p-value & 0.151203607862385 \tabularnewline
Lambda & -1.93034017436506 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=71427&T=3

[TABLE]
[ROW][C]Regression: ln S.E.(k) = alpha + beta * ln Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]-3.81114431583424[/C][/ROW]
[ROW][C]beta[/C][C]2.93034017436506[/C][/ROW]
[ROW][C]S.D.[/C][C]1.52925837657265[/C][/ROW]
[ROW][C]T-STAT[/C][C]1.91618383083995[/C][/ROW]
[ROW][C]p-value[/C][C]0.151203607862385[/C][/ROW]
[ROW][C]Lambda[/C][C]-1.93034017436506[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=71427&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=71427&T=3

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha-3.81114431583424
beta2.93034017436506
S.D.1.52925837657265
T-STAT1.91618383083995
p-value0.151203607862385
Lambda-1.93034017436506


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 12 ;
Parameters (R input):
par1 = 12 ;
R code (references can be found in the software module):
par1 <- as.numeric(par1)
(n <- length(x))
(np <- floor(n / par1))
arr <- array(NA,dim=c(par1,np))
j <- 0
k <- 1
for (i in 1:(np*par1))
{
j = j + 1
arr[j,k] <- x[i]
if (j == par1) {
j = 0
k=k+1
}
}
arr
arr.mean <- array(NA,dim=np)
arr.sd <- array(NA,dim=np)
arr.range <- array(NA,dim=np)
for (j in 1:np)
{
arr.mean[j] <- mean(arr[,j],na.rm=TRUE)
arr.sd[j] <- sd(arr[,j],na.rm=TRUE)
arr.range[j] <- max(arr[,j],na.rm=TRUE) - min(arr[,j],na.rm=TRUE)
}
arr.mean
arr.sd
arr.range
(lm1 <- lm(arr.sd~arr.mean))
(lnlm1 <- lm(log(arr.sd)~log(arr.mean)))
(lm2 <- lm(arr.range~arr.mean))
bitmap(file='test1.png')
plot(arr.mean,arr.sd,main='Standard Deviation-Mean Plot',xlab='mean',ylab='standard deviation')
dev.off()
bitmap(file='test2.png')
plot(arr.mean,arr.range,main='Range-Mean Plot',xlab='mean',ylab='range')
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Standard Deviation-Mean Plot',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Section',header=TRUE)
a<-table.element(a,'Mean',header=TRUE)
a<-table.element(a,'Standard Deviation',header=TRUE)
a<-table.element(a,'Range',header=TRUE)
a<-table.row.end(a)
for (j in 1:np) {
a<-table.row.start(a)
a<-table.element(a,j,header=TRUE)
a<-table.element(a,arr.mean[j])
a<-table.element(a,arr.sd[j] )
a<-table.element(a,arr.range[j] )
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Regression: S.E.(k) = alpha + beta * Mean(k)',2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'alpha',header=TRUE)
a<-table.element(a,lm1$coefficients[[1]])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'beta',header=TRUE)
a<-table.element(a,lm1$coefficients[[2]])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,summary(lm1)$coefficients[2,2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'T-STAT',header=TRUE)
a<-table.element(a,summary(lm1)$coefficients[2,3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-value',header=TRUE)
a<-table.element(a,summary(lm1)$coefficients[2,4])
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Regression: ln S.E.(k) = alpha + beta * ln Mean(k)',2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'alpha',header=TRUE)
a<-table.element(a,lnlm1$coefficients[[1]])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'beta',header=TRUE)
a<-table.element(a,lnlm1$coefficients[[2]])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,summary(lnlm1)$coefficients[2,2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'T-STAT',header=TRUE)
a<-table.element(a,summary(lnlm1)$coefficients[2,3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-value',header=TRUE)
a<-table.element(a,summary(lnlm1)$coefficients[2,4])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Lambda',header=TRUE)
a<-table.element(a,1-lnlm1$coefficients[[2]])
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable2.tab')